Additionally, for morphologically-rich languages, it can be difficult to predict the different word forms a given MWE type will occur across, posing a challenge for our requirement of no language-specific preprocessing.

in order to be consistent across all languages and be as language-independent as possible, we calcu- Score 1 for each language Score 2 for each language Figure 2: Outline of the method for combining distributional similarity scores from multiple languages, across the components of the MWE.


From the histograms, N = 6, N = 15 and N = 2 are the most commonly selected settings for ENC, EVPC and GNC, respectively.

the term is not in Panlex) the compositionality score for each target language is set to the average score for that target language across all other source language terms.


CS L1 +L2N : calculate distributional similarity over both the source language (CS L1 ) and the mean of the best-N languages (CS L2N ), and combine via the arithmetic mean.

following , we only consider the compositionality of the verb component in our experiments (and as such α = 1 in Equation 1).


Multiple annotators were asked to rate the compositionality of each German noun compound on an integer scale of 1 (non-compositional) to 7 (compositional).

the precision for our token matching method is very high, and the recall is also acceptably high.


To take the sentence structure into account, we want to encourage valid paths of tags during training, while discouraging all other paths.

additionally, all occurrences of sequences of numbers within a word are replaced with the string "NUMBER".


It has been shown that using word embeddings as features helps to improve general performance on many NLP tasks (Turian et al., 2010).

(2011), which reached 88.90% accuracy with a mix of unsupervised and supervised techniques to learn word vectors capturing semantic termdocument information, as well as rich sentiment content.


(2011), which reached 88.90% accuracy with a mix of unsupervised and supervised techniques to learn word vectors capturing semantic termdocument information, as well as rich sentiment content.

deep-learning architectures have shown their potential in several supervised NLP tasks, by using these word embeddings.


The pointer to the Wikipedia article serves as the correct concept for this mention and all other candi-date concepts we obtain from our lexicon as wrong concepts for this mention.

to us, most approaches for lexical disambiguation use either one model for all mentions (Milne and Witten, 2008;Ratinov et al., 2011) or a separate model for each mention or concept which requires a lot of training data (e.g.


(2006) produce a more computationally scalable version of the ideas presented in Pal and Narayan (2005) by associating a context vector with each document, with a fixed set of topics (12 in their case), for which they learn these context vectors using a naive Bayes classifier.

it also incorporates the age of documents.


Regions, on the other hand, prefer the PRE-CEDE ordering.

behind the horse is a man laying down on his back completely flat and straight.


(2013) similarly note that generated REs should avoid information that is perceptually expensive to obtain.

these include the number of dependents (landmarks which relate to each object in the description) and the number of descendants (the direct dependents, their dependents and so forth).


This study focused on predicting speaker behavior, while acknowledging that the utterances speakers produce are not always optimal for listeners .


Finally, we intend to incorporate the visual features used in this study into a full-scale realization system.

it makes sense that neither large or small objects favor the use of this construction; it can be used to foreground an object which is not salient enough to be assumed in common ground, but is salient enough to find without a great deal of visual search.


2 Despite our instructions to provide single-word substitutes, 11,337 substitutions contain more than one word.

the common core for all parasets of targets that map onto two or more synsets contains only around 0.4 substitutes (see row 3) -that is, it is empty more often than not.


On the opposite, Krippendorff (2004b) claims that this 0.67 cutoff is a pretty low standard while Neuendorf (2002) supports an even more restrictive interpretation.

these questions have mainly been investigated from a theoretical point of view.


We primarily rely on the informedness measure (Powers, 2007) for quantifying performance.

for filtering, all patterns that do not contain two 'NNP' tags and appear less than 60 times are removed (the filter threshold is increased as POS patterns are more generic).


According to the reported subsequence results, combining lexical features with part-of-speech features counter-intuitively lowers the performance when using SVM or Naïve Bayes and the positive effect on J48 was shown to be insignificant (p>0.68).

in this subsection we design the Shortest Path feature model, a model aiming at exploiting some of the specific properties of place analogies.


This intuition was supported by a short evaluation in which we sampled 314 (10.3%) random snippets from this set and found none that had been misclassified.

to the best of our knowledge, no such corpus is freely available.


Crowd-sourcing has been shown to be effective for language processing related tasks, e.g.

for SVM-based classification, the higher reported performance for also including POS features in addition to lexical n-grams could not be shown to be significant (p>0.4).


Table 4 shows that only UNSPLIT and STR (source language and a reduced set of target language features) are significantly 9 improving over the RAW baseline.

the same applies for "Bußgeld", a synonym of "Geldstrafe", for which the UN-SPLIt baseline selected "schönen" ("fine, nice") instead.


As the source language input is fluent, these features are more reliable than features derived from target language SMT output.

additional manual evaluations showed that compound processing leads to improved translations where the improvement is not captured by BLEU.


This way, the training data was less fluent than in its original format, but still of higher quality than SMT output of unseen data.

"polar bears", "brown bears", "bear skin", "bear fur") to obtain only 2: occurrences in raw training data: Bär (19), Bären "bär" occurring in underspecified split data: Bär<+NN><Masc><Sg> 94Bär<+NN><Masc><Pl> 29"Emissionsverringerung" (cf .


(2012) we re-implemented the approach of Stymne and Cancedda (2011), combined it with inflection prediction and applied it to a translation task.


it is more precise when producing compounds.

we adapted it to work on tokens: some words can, depending on their context, either be interpreted as named entities or common nouns, e.g., "Dinkelacker" (a German beer brand or "spelt|field").


To formalise this idea into a generative model, we assume that the Giver act g depends only on the Follower act f .

for example, in a flight booking domain, if a user's goal is to fly to London from New York on the 3 rd of November, then the goal takes the form: {origin="New York", dest="London", depart date="03-11-13"}, and expressing the destination takes the form: Provide dest="London".


During test time, the difference between actual and predicted length allows us to quantify text verbosity.

the next section describes the features used for indicating the content type of a snippet.


This work was partially supported by a NSF CA-REER 0953445 award.

we use data commonly employed to develop statistical models for sentence compression (Knight and Marcu, 2002;McDonald, 2006;Galley and McKeown, 2007).


We expect that EXPANSION relations should be more likely to have coreferential pairs because the detail or information about an entity mentioned in Arg1 should be expanded in Arg2.

those word pair features work well in this case because their artificially generated dataset is an order of magnitude larger than PDtB.


When pragmatic information is used, 5.6 relevance variables are used on average (per dialogue snipped).

we construct this objective function by associating each decision with a feature vector, obtained using a feature function φ (described in Section 2.3.4), mapping the relevant part of the input to a feature set.


We marked the parent (POS tags) and grand parent (chunk) nodes of such lemmas by prepending a REL tag.

we look for token sequences (e.g., complex nominal groups) in Q/AP pairs that refer to entities and entity classes related by isa (Eq.


The predicted labeling follows from these scores asŷ i = arg max j≤c F ij .

it cannot only be observed with the relation SuitsTo, as in (3) (66% of all instantiations of that pattern), but also SubstitutedBy (20% of all mentions of that relation match that pattern), as in (4).


As this leaves no unspecified food items in our vocabulary, we cannot use the output of HEUR as seeds for graph-based optimization.

we train a supervised classifier and incorporate the knowledge induced from our domain-specific corpus as features.


These relation types are highly relevant for customer advice/product recommendation.

it depends, however, on an automatically constructed high-quality similarity graph.


In this paper, we focus on the third part while assuming the first two are given.

the, to) while AGR prefers keeping words specific to e (e.g.


Determining whether a certain word/phrase is redundant is a stylistic question, which is difficult to quantify.

to obtain baselines that are as strong as possible, we tune the weight α on evaluation data for best accuracy.


This inconsistency is exacerbated by raters recruited via crowdsourcing platforms as in our study (Koller et al., 2010;Rieser et al., 2011).

the interaction manager, and that the task of the surface realiser is to find a suitable stylistically appropriate realisation.


Words that tend to be used in the summaries, characterized by high KL(A ∥ G) scores, include locations (York, NJ, Iraq), people's names and titles (Bush, Sen, John), some abbreviations (pres, corp, dept) and verbs of conflict (contends, dies).

the performance of our logistic regression model is evaluated on two tasks: keyword identification and extractive summarization.


4 Part-of-speech tags are the ones assigned by our cost-sensitive (CM) POS model trained on Twitter data, the concatenation of Gimpel and 70% Ritter training data.

the measure used in this paper is less affected by these factors, but manages to give us a good understanding of the agreement.


All  Table 2: Downstream results on chunking.

parameters were optimized only on POS tagging data, not on the downstream evaluation tasks.


one for each attention vector set) are concatenated for classification.

our NTI model does not rely on either a parser output or a fine-grained supervision of nonleaf nodes, both required in previous work.


RNNs equipped with gated hidden units and internal short-term memories, such as long shortterm memories (LSTM) (Hochreiter and Schmidhuber, 1997) have achieved a notable success in several NLP tasks including named entity recognition (Lample et al., 2016), constituency parsing (Vinyals et al., 2015), textual entailment recognition (Rocktäschel et al., 2016), question answering (Hermann et al., 2015), and machine translation (Bahdanau et al., 2015).

we describe in this section experiments on three different NLP tasks, natural language inference, question answering and sentence classification to demonstrate the flexibility and the effectiveness of NTI in the different settings.


As an alternative, we present and evaluate new sequencepreserving ways of attention.

attention can be thought of as weighted selection: some input elements are highly weighted, others receive weights close to zero and are thereby effectively not selected.


It is not only not indexed and isolated, but also requires a specific software or a dedicated proxy server to access it.

3 The Dataset To best of our knowledge, there is no labeled dataset that encompasses the activities on the Darknet web pages.


We have used the EurWordNet list of ontological types for senses (Vossen et al., 1998) to convert the SUPERSENSES into coarser semantic traits like Animate or UnboundedEvent.

(2016), who focus on the effect of auxiliary data size (constituency parsing) on the main task (machine translation), finding that large amounts of auxiliary data swamp the learning of the main task.


Auxiliary task level distribution also plays a role, as we will discuss in Section 4.3, FREQBIN-UNIFORM consistently outperforms the skewed measure with base 5 and 10.

they all have in common that their O class is highly indicative of function words, an argument supported by their similar values of fulldistribution entropy.


POS is the label set with the highest explanatory power over frequency, which is expectable: determiners, punctuations and prepositions are high-frequency word types, whereas hapaxes are more often closed-class words.

dEPRELS, coming from the same corpus, has about three times as many labels, yielding a distribution that has fewer mid-values while still being less than 3.


While the original methods were based on count-based vectors, in recent years they have been used with word embeddings (Mikolov et al., 2013;Pennington et al., 2014), and have gained popularity thanks to their ease of use and their high performance on several common datasets.

we experimented with two feature weightings: • Frequency -raw frequency (no weighting): M i,j is the number of co-occurrences of w i and c j in the corpus.


Besides, authors propose two novel algorithms: Balanced Maximum Spanning Tree Clustering and Squares (B-MST), Triangles and Diamonds (SquaT++).

to word sense induction by context clustering (Schütze (1998), inter alia), our method constructs an explicit word sense inventory.


In our pipeline we do not make any distinction between evaluation datasets and senseannotated training corpora, as the pipeline can be applied equally to both types.

the field seems to be slowing down due to the lack of groundbreaking improvements and the difficulty of integrating current WSD systems into downstream NLP applications (de Lacalle and Agirre, 2015).


Despite the simplicity of it, IR approach achieves a promising result in experiment.

as there are almost 10 million students take the examination every year, Gaokao needs to be difficult enough to distinguish the excellent students.


The update equations are as follow: In the above equations, w t denotes the t-th word in the background sentences, GRU is defined in equation (19)(20)(21)(22), h t−1 and h t are the hidden representation of w t−1 and w t respectively, l stands for the lead-in sentence encoded by the sentence encoder, • is element-wise multiplication and m t is the computational result of current step.

another nonnegligible task is machine comprehension, also called reading comprehension.


Both of Long-Short Term Momery (LSTM) (Hochreiter and Schmidhuber, 1997) and Gated Recurrent Unit (GRU) (Cho et al., 2014) perform much better than the standard tanh RNN.

sentence Encoder that encodes lead-in sentence, sentences in permanent memory and answer candidates.


This paper is organized as follows: an overview of previous work on non-factoid question answering is provided in Section 2, our neural architecture is introduced in Section 3, the discourse features that are incorporated into our neural approach are described in Section 4, the results of our experiments with these new models are presented and analysed in Section 5, and suggestions for further research are provided in Section 6.

despite containing only how questions, the dataset contains a large amount of questions asking for an opinion or advice , e.g.


The combined approach achieves new state-of-the-art results on two CQA datasets.

gold: Try the following code sudo shutdown -P now ( ...) -P Requests that the system be powered off after it has been brought down.


These neural approaches aim to obviate the need for any feature engineering and instead focus on developing a neural architecture that learns the representations and the ranking.

one possible reason is that the latter uses a large corpus for unsupervised pretraining.


First, the answer is searched for discourse markers.

to the (Bogdanova and Foster, 2016) approach, which is also purely neural but requires a large in-domain corpus for pretraining, our model requires only a relatively small training set and no pretraining.


The scores of max and LSE pooling are significant with (p ă 0.02).

dramatic improvement in generalization can be obtained by reasoning about paths, not in terms of relation-symbols, but Universal Schema style relation-vector-embeddings.


For a recent and extensive survey of RNNs in NLP see (Goldberg, 2015).

compare the previous example with "Aspirin cured my sleepiness but gave me a terrible headache", while the medical concepts are similar, their context determines their particularly associated type label.


We would like to thank the IBM Almaden Intelligence Augmentation Team (Alfredo Alba, Linda Kato, Chris Kau, Joe Terdiman and Steve Welch) for many fruitful discussions, and the anonymous reviewers for their helpful comments.

we use a bi-directional LSTM transducer (Graves, 2012) which outputs a probability distribution over the three possible labels (B, I, and O) per word, taking into account arbitrary length contexts from both past as well as future words.


Each hidden layer uses a rectified linear unit as non-linearity.

here, gender prediction does not benefit from including mental conditions, but helps vice versa.


the arguments are convincing: Predicting multiple related tasks should allow us to exploit any correlations between the predictions.

social media text carries implicit information about the author, which has been modeled in natural language processing (NLP) to predict author characteristics such as age (Goswami et al., 2009;Rosenthal and McKeown, 2011;Nguyen et al., 2014), gender (sarawgi et al., 2011;Ciot et al., 2013;Liu and Ruths, 2013;Volkova et al., 2015;Hovy, 2015), personality (schwartz et al., 2013;Volkova et al., 2014;Plank and Hovy, 2015;Preoţiuc-Pietro et al., 2015), and occupation (Preotiuc-Pietro et al., 2015).


12 Computational complexity is not an issue for standard semantic benchmarks such as SimLex-999 or MEN: these data sets require only N gt similarity computations in total, where N gt is the number of word pairs in each benchmark (999 or 3000).

in addition, the metric should still reward models that rank relevant responses higher than non-relevant ones.


Following that, the correlation scores are averaged.

each cue word w c can be thought of as an input query issued against some target concept collection V r , where the goal of our association retrieval system is to rank items from the target collection according to their relevance (i.e., their association strength) to the issued query.


Quality is assessed for well-arranged discussions that seek agreement.

5 The study results were discussed by all annotators, leading to a considerably refined version of the guidelines.


Parsey also features almost identical performance in both attaching and labeling tokens.

additionally, a system that performs better than a baseline is bound to make errors where the baseline was correct.


attaching correctly resolved mentions to incorrect antecedents (tp → wl).

8 one third of the changes that Clevertagger introduces are errors.


These demonstrate that we need to better understand the drawbacks of existing automatic evaluation metrics.

moreover, we explore the utilization of the recently proposed Word mover's Distance (WmD) document metric for the purpose of image captioning.


Then, the co-occurrences of n-grams in the reference sentences and candidate sentence are calculated.

the distance between word i and word j in two documents is set as the Euclidean distance between each of the corresponding word2vec embeddings The distances between words serve as building blocks to define distances between documents, hence captions.


Overall, Formality-RB obtains the highest correlation score (80.7%) among all features.


Results of our first experiment indicated strong correlations between quality scores of the features and human rankings.

results Table 3 shows all the Pearson's correlations.


METEOR employs both word matching scores and the linguistic information (e.g., synonyms and stemming) in contrast to BLEU.

we also investigate whether we can predict human rankings of MT translated text by combining these features since they capture different aspects of translation.


Dependency parsing is an integral part of many natural language processing systems.

hence, this corresponds to assuming neither positive nor negative evidence concerning the edge.


In the monolingual setting, we compare our parser to TurboParser (Martins et al., 2010) -a fast, capable graph-based parser used as a component in many larger systems.

most research into dependency parsing has focused on learning from treebanks, i.e.


Complementarily, we can measure the contribution of PR by observing the difference between regular UDP and UDP N oP R .

they also had a special connection to some extremists canonical language name (e.g., Finnish instead of Finnish-FtB).


Data-driven unsupervised dependency parsing is now a well-established discipline (Klein and Manning, 2004;Spitkovsky et al., 2010a;Spitkovsky et al., 2010b).

nevertheless, the baseline is oracle-informed about the language's best branching direction, a property that UDP does not have.


Our system is a dependency parser that requires no training, and relies solely on explicit part-ofspeech (POS) constraints that UD imposes.

the parser has very few parameters and is distinctly robust to domain change across languages.


By and large, these presumptions are unrealistic and exclusive to a group of very closely related, resource-rich Indo-European languages.

the system cannot infer relative clauses.


Both POS show large standard deviations, which indicates great variability.

if we regard the results for UDP G in Table 5, we can see that there are 24 languages (out of 32) for which the parser performs better than for English.


While previous work have attempted to learn multilingual word clusters or embeddings (Guo et al., 2015) and use these for cross-lingual transfer, this paper explores a different research direction.

this gives vocabulary sizes ranging from a hundred or less for analytic languages (English has about 120 such combinations) to several thousands for synthetic ones (the Finnish part of the UD Project has about 4000 such combinations).


This issue is challenging, and only recently addressed from computational linguistics.

informed by linguistic theories, we propose for the first time a multi-layered annotation schema for irony and its application to a corpus of French, English and italian tweets.We detail each layer, explore their interactions, and discuss our results according to a qualitative and quantitative perspective.


This choice was motivated by the intention to increase the number of opinionated posts and therefore to have a variety of aspects to be analysed.

we report in Table 4, the inter-agreement between couples of annotators distinguished for each label.


Sentiment Analysis is a broad task that involves the analysis of various aspect of the natural language text.

given a post p about a given topic t, its subjectivity or objectivity can be defined as follows (Liu, 2012): An objective post p o presents some factual information about the world, while a subjective post p s expresses some personal feelings, views, or beliefs.


While classifying explicit discourse relations is relatively easy, as the discourse connective itself provides a strong cue for the discourse relation (Pitler et al., 2008), the classification of implicit discourse relations has proved to be notoriously hard and remained one of the last missing pieces in an end-to-end discourse parser .

we focus on the second-level 11-way classification because the labels are fine-grained enough to be useful for downstream tasks and also because the strongest neural network systems are tuned to this formulation.


These results show that using all the words in the units (Ji and Eisenstein, 2014;Li et al., 2014), is not as useful as using more contextual information, that is taking more DUs into account (left and right children of the CDUs in the stack).

in the initial configuration, S is empty and B contains the whole document.


Both T A and T C are learned during training in an end-to-end fashion.

furthermore, current approaches and available datasets for state tracking do not explicitly cover reasoning capabilities such as temporal and spatial reasoning, counting, sorting and deduction.


However, the main difficulties in applying these tests are: (i) time required, since it is a manual task; and (ii) the subjectivity of the clinician.

the LStM updates for time steps t are done as described by Jozefowicz et al.


I.e., the POS tagger and parser are trained on random splits (random baseline).

for our experiments, we use sentences as documents.


The closest approach to ours is the one by Plank and van Noord (2011), who employ a similar idea of using topic modeling for creating parsing experts.

this shows very dramatically that the two topics learn different patterns.


I.e., we assume the same number of random "topics", all of the same size.

both corpora use the Penn Treebank POS tagset (Santorini, 1990) with minor differences: The tagset used in GENIA is based on the Penn Treebank tagset, but it uses the tags for proper names and symbols only in very restricted contexts.


The gain in UAS is considerably smaller: The topic modeling expert reaches 90.55% as opposed to 90.26% for the full baseline.

here we investigate whether LDA can separate the sentences into meaningful topics.


Here we investigate whether LDA can separate the sentences into meaningful topics.

their task is to determine in a domain adaption setting which sentences of an outof-domain training set are the most similar to the test set.


In this setting, we can see if the experts can effectively handle the data sparseness caused by dividing the training set into multiple experts.

even if the topic model POS tagger outperforms its full baseline, the learned adaptations only have a minimal effect on parsing accuracy.


Also, we would like to point out that the utilization of language-specific labels does not contradict the UD principles.


Our UD principles introduced in this paper follow the central UD guidelines (Nivre, 2015) and we did our best to align with the existing guidelines for other morphologically rich languages as well.

sentences containing an overt or covert form of the copula could be parsed more effectively by using universal morphology codes.


While they use a 3-layer deep LSTM, we only use a single-layer LSTM for both encoder and decoder since our data is relatively small and empirical comparison shows that stacking more layers doesn't help in our case.

barzdins and Gosko (2016) † is the word-level neural AMR parser, barzdins and Gosko (2016) is the character-level neural AMR parser.


Also, if we set n too high, although the output vocabulary size becomes smaller, some of the frequent output labels that the model handles well originally will be put into the coarse-grained types, losing information in the recovery process.

table 2, the supervised attention model is able to further improve the Smatch score by another 2 points, which are mainly contributed by 3 points increase in recall.


In this paper, we present a method which linearizes AMR graphs in a way that captures the interaction of concepts and relations.

the output AMR sequences usually have parentheses that do not match.


Moreover, the enumeration of the oracle summaries for a given reference summary or a set of reference summaries is essential for automatic evaluation.

in this paper, we focus on extractive summarization, employ ROUGE n as an evaluation measure, and define the oracle summaries as follows: (2) D is the set of all the sentences contained in the input document(s), and L max is the length limitation of the oracle summary.


(2012) suggested using ROUGE 1 and keeping stopwords.

our experimental results obtained from Document Understanding Conference (DUC) corpora demonstrated the following: (1) room still exists to improve the performance of extractive summarization; (2) the F-measures derived from the enumerated oracle summaries have significantly stronger correlations with human judgment than those derived from single oracle summaries.


where h q l and h a l are the question and the answer encoded vectors and o QA denotes the output of the hidden layer of the MLP.

nSE uses the vector dot product for the similarity measure in nSE because it is faster to compute.


Our NSE models outperformed the previous stateof-the-art models in terms of both accuracy and MSE, by approximately 2-3%.

the initial state of the NSE memory stores information about each word in the input sequence.


Tables 1 and 2 show the average number of queries and success rates for algorithms using our hierarchical model vs the Gaussian distribution based model.

this approach usually requires querying each competing system with a large number of documents, which can be problematic if either the number of test documents is limited, or the systems are implemented as APIs by a third party and performing each query incurs a cost.


For English, adding random information had a negative impact on the performance with respect to both the baseline and the CW model.

much research has also been done on modeling other types of subword information (e.g.


In the remainder of this paper, 'wx' = means word embeddings of size x for a word-level model and 'cx' means character embeddings of size x for CW models.

they only compare to a simple baseline model of 2 LSTM layers of each 200 hidden units without dropout, resulting in a higher baseline perplexity (as mentioned in section 4.3, our CW model also achieves larger improvements than reported in this paper with respect to that baseline).


As mentioned above, Shen and Lee (2016) employed an attentional RNN for independent DA classification; they achieved an accuracy of 72.6% on textual transcriptions of the Switchboard corpus.

the sequence-labeling task involves learning a model that maps an input sequence to an output sequence.


The LSTM basis function is calculated with a fixed vector g g g 0 instead of the previous time step's vector.

the results in table 2 show that our model outperforms these baselines.


(2014) proposed an attentional encoder-decoder architecture for machine translation.

as seen in Table 5, both models yield a negligible or no drop in accuracy on the MapTask development set, while both yield a drop in accuracy on the training set.


To the best of our knowledge, this is the first end-to-end NNbased model that can conduct meaningful dialogues in a task-oriented application.

(2015), we tie the RNN weights together for each value v but vary features f t v when updating each pre-softmax activation g t v .


A dialog modeling approach (Young et al., 2013) is also relevant to our task.

the first baseline is an information-retrieval approach based on a tF-IDF match (Salton et al., 1975) which finds the most similar conversation in the training data, and returns the associated order.


WSABIE uses WARP loss and gradient-based updates to minimize the distance between the latent representations of the predicate target and the correct frame, while maximizing the distance to all the other irrelevant frames.

if the predicate is unknown to the lexicon, p / ∈ L, the overall bestscoring frame is chosen.


The number of targets in the 4,077 tweets to be annotated originally amounted to 12,874.

firstly we adopt the context-based approach by Vo and Zhang (2015), which divides each tweet into three parts (left context, target and right context), and where the sentiment towards a target entity results from the interaction between its left and right contexts.


We measure inter-annotator agreement in two different ways.

the annotators unhighlighted 975 of them, and added 688 new ones, so that the final number of targets in the dataset is 12,587.


Indeed, when we simply combine the two datasets in the TE setting, we notice a sharp drop in performance (compared to Table 3).

all word problems in the linear-T2 subset of the DOLPHIN dataset can be solved using linear equations.


Table 1 shows our notation, where our proposed annotations are shown in bold.

in Table 4, we compare TE * and TD settings.


Extracting Equations A word problem on algebra.com is accompanied by a detailed explanation provided by instructors.

bottom-up approaches (Hosseini et al., 2014;Shi et al., 2015;Koncel-Kedziorski et al., 2015) jointly infer the derivation z = (T, A).


In addition to being suffixing, three of these languages employ vocalic (German, Spanish) and consonantal (Russian) stem changes for many inflections.

(iv) NOFORM is the case where the target form cannot be regularly derived from any of the source forms.


The output is generated from this input.

(2008) develop a high-performing weighted finite-state transducer for the task, which was later hybridized with an LSTM (Rastogi et al., 2016).


As the starting point varies, MRI is more difficult than morphological inflection and exhibits more data sparsity.

our datasets are based on the data from the SIGMoRPHoN 2016 Shared Task on Morphological Reinflection (Cotterell et al., 2016a).


The single-source input poner, Inf, does not reveal the stem for the subjunctive: it is compatible with both ponga and pona-we need both source forms to generate the correct form ponga.

to our work, they obtain a single output for each input.


In combination with the source and target forms of the original dataset, this means that our dataset is a set of 5-tuples consisting each of four source forms and one target form.

even for g, the source form that is least relevant for generating wögen receives almost no weight: wägst is an indicative singular form that does not provide helpful information for generating a plural form in the subjunctive; the model seems to have learned that this is the case.


A significant gain in performance (+1.04 BLEU, -1.16 TER points) is obtained by our online IS system that, by using the instance selection technique, is able to isolate only the most useful training samples.

for this purpose, the retrieved samples are randomly split three times into training and development.


The size of this development set is critical: if it is too large, then parameter optimization will be expensive.

the MT output left untouched), ii) the batch APE system described in Section 3, on top of which we incrementally add our online learning extensions, and iii) the Thot toolkit.


As we will see in Section 5, although our use of negative feedback is still at a preliminary stage, its integration in our online APE framework yields some improvements.

if it is too small, the tuned weights might not be reliable.


Furthermore, each node can have at most three parent nodes, created by transitions RArc, LArc and Reduce, respectively.

similarly, we train a binary classifier for deciding whether or not to create a reentrant edge after a Reduce: in this case we use word and POs embeddings for the two nodes being connected and their parent as well as dependency label embeddings for the arcs between them.


Hence, we also expected batchwise learning to outperform full learning.

the following experiment demonstrates that using multiple parses when updating the state improves overall dialog success.


A demonstration of this can be seen in tables 4 and 5.

these systems typically assume a fixed natural language understanding component.


, c |C| , and the corresponding dependency parse as a list of dependency word nodes D |D| .

to make the alignments fit the CAMR parser, we convert both ISI and our alignments to the original JAMR alignment format, word span to AMR concept.


In practice, the number of case frames is always larger than the number of sense in PropBank for each verb.

all correct semantic roles are used as positive examples and the incorrect ones are used as negative examples for semantic role selection.


FIGER uses a wide variety of features on different levels (including parsing-based features) from contexts of entity mentions as well as the mentions themselves and returns a score for each mention-type instance in the corpus.

we represent entities on three levels: entity, word and character.


(2016) also address a similar task,  but they rely on entity descriptions in KBs, which in many settings are not available.

the bag-of-word context is difficult to distinguish for pairs of types like (restaurant,food) and (author,book).


RQ2 What is the quality of the resulting taxonomies?

contrastMedium, instead, after the shaking of the graph can leverage the partial ordering of the nodes (based on the concept granularity of the corresponding concepts) to select the edges (animal, feline), (feline, lion) and (animal, great apes), while removing all remaining wrong and redundant edges.


For example, bridge:NN:3 is linked to the Babel synset bn:00013077n (the 'infrastructure' sense).

the observed quantities are then transferred to corresponding nodes of the noisy graph by the mapping M .


We acknowledge the support of the Deutsche Forschungsgemeinschaft (DFG) under the JOIN-T project.

propagated topological clues from KB are additionally leveraged to ensure that the output ALGORITHM 1: The ContrastMedium algorithm.


We propose Algorithm 1 as an approximate heuristic.

the software generates an hop-1 query q 1 = x for each x ∈ X , finds the entity e 1 that aligns with q 1 , and then finds triples matching (e 1 , r 2 , ?).


Formally, we define for ith entity the popularity variable q i (u) = Σ m I(u m = i) and the potential for EP factor Ψ EP i (u) as follows: The parameter α > 0 is initially fit from Freebase entities and then finetuned with TAC 2014 dataset to reflect real-world distributions.

most entities have only a small number of variants and are mentioned only a few times.


Besides, we introduce an attention layer into the proposed attribute-to-sequence model.

for example, we could promote a product to users who have not bought it, by generating novel and personalized recommendations.


As shown in Figure 1, our model contains three parts: attribute encoder, sequence decoder, and an attention mechanism.

these automatically generated reviews are useful for companies.


We build a publicly available dataset for the review generation task by leveraging the Amazon book reviews and their metadata.

we have 80, 256 books, 19, 675 users, and 5 rating levels.


The reviews whose lengths are greater than 60 words are filtered.

our model is mainly evaluated on the review generation task rather than classification.


We report on the task of generating textual summaries for people, mapping slot-value facts to onesentence encyclopaedic biographies.

kBs have been used to investigate the interaction between structured facts and unstructured text.


Our model as presented does not make this assumption (it does not always produce a proper name reference that distinguishes the target from the distractors).

table 1 depicts the discourse features used to describe the proper name references.


Additionally, we found that participants preferred the original over the regenerated texts.

pN+Variation takes variation into account by applying the distribution of proper name forms obtained from the training set to the similar references in the test set, as explained in Section 4.1.


The result is that some ungrammatical sentences have lower processing cost and higher acceptability than some complex grammatical sentences: with doubly nested relative clauses, for instance, subjects rate ungrammatical sentence (1) as more acceptable than sentence (2), forgetting about the VP predicted by the second noun (Gibson and Thomas, 1999).

the surprisal-mitigating effect of a context element on a word w i decreases as that element gets farther from w i .


Memory-based models of sentence processing account for apparent dependency locality effects, which is processing cost apparently arising from two words linked in a syntactic dependency appearing far from one another (Gibson, 1998).

to see this, consider only the lowest-order interaction information terms in Equation 7, truncating the summation over n at 1.


Under this assumption, for which we provide evidence below, dependency locality effects can be seen as a special case of information locality effects.

both the English and German behavioral patterns are quite generally predicted by the model.


We develop a deep neural network (DNN) to learn representations of phrases of arbitrary lengths.

following Lai and Hockenmaier (2014), we train our final system (after fixing of hyperparameters) on train and dev (4,934 pairs).


(2015) study the characteristics of linguistic relations that may signal entailment or contradiction at subsentential level.

there are two possible types of axioms that can be created: either entailing axioms ∀θ.p , where θ = θ j ∪ θ i is the union of variable names occurring in θ j and θ i .


To gain robustness and following Abzianidze (2015), we use a multiparsing strategy (unless stated otherwise), that is, we use both C&C and EasyCCG parsers, and output any of their judgements if they are different from "unknown" 3 .

one could use other databases, ontologies or statistical classifiers, but we leave those considerations out of the scope of this paper.


Vilnis and McCallum (2015) use Gaussian functions to map a word to a density over a latent space.

in example 6, it is not certain that the people are playing music, but it is a reasonable assumption from the premise.


The goal of textual entailment is to predict whether a hypothesis sentence is true, false, or neither based on the premise text (Dagan et al., 2013).

we use 300d GloVe vectors (Pennington et al., 2014) trained on 840B tokens as the word embedding input to the LSTM.


We use the same model to represent both x and y regardless of which phrase is the premise or the hypothesis.

for evaluation purposes, we first assign 5% of the phrases to the development pool and 5% to the test pool.


(2014) introduced the concept of "denotational similarities" to capture which expressions can be used to describe similar situations.

their systems can only use deno- Figure 2: An embedding space that expresses the individual probability of events X and Y and the joint probability P (X, Y ).


Our model's predictions are less accurate when the sentence structure differs substantially between premise and hypothesis, or when there are many unknown words, as in example 5.

we learn a mapping from a phrase The origin (the zero vector) therefore has probability exp(0) = 1.


A rating of 1 means "not like me at all" and 6 means "very much like me".

(2014), this corpus was used to develop an SVMbased speech act classifier using the following features: bag-of-words (top 20% bigrams), presence of "wh" words, presence of question marks, occurrence of "thanks/thanking" words, POS tags distributions, and sentiment lexica such as the NRC lexicon (Mohammad et al., 2013), SentiWordNet (Baccianella et al., 2010), and WordNet Affect (Strapparava and Valitutti, 2004).


Presently we are analysing the community detection problem in social media in relation to values.

the intrinsic value of the lives we lead reflects the strength of our values and ethics which guide our social practices, attitude and behaviour.


We have methods to extract arguments that represent different stances on an issue (Misra et al., 2016a;Anand et al., 2011;Sridhar et al., 2015;Walker et al., 2012a;Walker et al., 2012b), as well as argument exchanges that are agreements vs. disagreements , factual vs. emotional arguments (Oraby et al., 2015), sarcastic and not-sarcastic arguments, and nasty vs. nice arguments (Oraby et al., 2016;Lukin and Walker, 2013;Justo et al., 2014).

as expected, opinion change is greater for people who are initially more neutral about a topic, than those who are entrenched.


We provide a new corpus for the research community of audience personality profiles, arguments, and belief change measurements.

we vary whether providing a learner with features about a person's personality improves performance for predicting belief change, when compared with providing information about degree of entrenchment alone.


In addition to the forward pass, the Char-Bi-RNN also processes the character sequence backwards (symbolised by ← − h c i,j ) with another set of GRU weight matrices and bias terms.

this sacrifices the meaning of the unknown word which may be critical.


We therefore exclude these algorithms for this study, also in order to focus our analysis and make the comparison with traditional word alignment algorithms more straightforward.

up until now, we used bilingual data to train crosslingual embeddings, even though our parallel corpus (the Bible) is in fact multi-lingual.


In absolute terms, Multilingual SID-SGNS's performance is still very low.

we suggest that Dice uses the sentence-ID feature set naïvely, resulting in degenerate performance with respect to the other methods.


S1-S3 indicates sentences where only a subset of labels for entity mention (bold typeface) are relevant given context, highlighted in T1-T3.

we illustrate this through an example in Figure 1.


There has been considerable amount of work on Named Entity Recognition (NER) (Collins and Singer, 1999;Tjong Kim Sang and De Meulder, 2003;Ratinov and Roth, 2009;, which classifies entity mentions into a small set of mutually exclusive types, such as Person, Location, Organization and Misc.


It is specifically appealing for processing large-scale social media data.

different from the previous methods, our model can automatically identify the number of events in the corpus and deal with lexical variations of named entities using word embeddings generating from neural language modelling.


However, the size of the cluster returned by DPEMM is smaller and it failed to extract the location information.

as such, we select event elements from tweet clusters not only based on their probability distributions given by the proposed models but also taking into account their cooccurrences in each tweet cluster.


Evaluation was carried out on a real-world dataset consisting of event reports and tweets collected over four months from San Francisco Bay Area.

the purity of clusters generated by DPEMM is slightly higher than that generated by DPEMM-WE.


It might be attributed to the property of DPEMM  that the cluster is generated dynamically without a preset number of clusters.

although DPEMM-WE gives better extraction results overall compared to DPEMM as shown in Table 2, it returns lower purity results because of some noisy information introduced through word embeddings.


Table 3 shows true annotations of all word pairs in this sentence as required for training the AWP-NN model.

we also propose to refine output of the AwP-NN model by using inference in Markov Logic Networks (MLN) so that additional domain knowledge can be effectively incorporated.


We observed that for some entity mentions, it is very difficult to identify their entity types as the key information required for identification lies outside the current sentence.

end-to-end relation extraction deals with plain sentences without assuming any knowledge of entity mentions in them.


Our one-step CRF-based approach may thus be misled by those common words into forcing the presence of a dependence relation.

we approach the entity extraction (probability term and variables on one side, influence terms and variables on the other side) as a sequence labeling problem.


Afterwards, if the confidence of the classifier is below p 2 , we discard the sentence, if it is above p 1 we enforce our constraints regardless of the Baseline CRF output.

for probability statement extraction, for a sentence such that |t P | = 0, then we define a modified output y B (B standing here for Baseline) where y B t = O ∀t = 1 : T , else we keep the original output so that y B t = y * t ∀t = 1 : T .


In the case of dependence statement extraction, the situation is similar, if we detect an influence term or a variable, we want to force the system to find the other relevant pieces of the relations.

we caution the reader that our use of the word "dependence" is exclusively related to its meaning in probability theory.


SW+D+CNN-11/22/55K are the results when the similarity measures comes from SW+D as mentioned in Section 6.1 and also using the word vectors from encoding matrix A obtained after training on CNN-11/22/55K.

the requirement of human generated annotations for training, in order to ensure a sufficiently diverse set of questions is prohibitively expensive.


For reasons of space, we do not provide detailed explanations and formulas for the nine metrics, but refer readers to the cited papers.

(1992) but has attracted relatively little attention since then.


Unlike SemEval-2007, the SemEval-2012 task is concerned exclusively with ranking substitutes; all the original participating systems were given the gold-standard substitutes and simply asked to put them in the correct order.

we consider these two naïve baselines as reasonable lower bounds.


We used Groundhog 1 as the implementation of the NMT system for all experiments.

instead, a path from English to French to English can be used.


As we keep adding more languages to the model, the hidden layer in our model -shared between all languages -might not be enough to accommodate all languages.

(2015) achieved a state-of-the-art result for en→de and de→en respectively, served as the reference.


(2015) adapted for multiple languages also using bilingual corpora.

for the first step, instead of just predicting the translation for a single target language, we predict the translation for all languages in the lexicon.


(2008a), which is also used for building a semantic representation (Zesch et al., 2008b).

a complete disambiguation reduces the composition to a single concept.


As their goal is to find an approximation for the MCS baseline, they try to find the sense embedding that is closest to the embedding of the target word.

the main decision factor is the time, which grows exponentially with respect to the length of the windows.


In this section, we assess the performance of language models on varying quantities of data, across a number 1,000 10,000 100,000 200 of different source-target language pairs.

we now proceed to discuss the challenges of this data to explore why the positive results of language model pre-training that were seen in Section 4 were not seen in this experiment.


For all languages, interpolating MKN with the CLWE (Interp.)

semantically identical words may take different surface tones than is present in the relevant lexical entry, resulting in mismatches with the lexicon.


， 鼓励 高级 专 专 专员 员 员 确保 作出 适当 安排 和 提供 预算 资源 Reference: commends the high priority given by the office of the united nations high commissioner for human rights to work on national institutions , [.

in some cases, no hypothesis conforms to the consistency decision, and in this case post-editing the best hypothesis appears to be beneficial.


Levy (2008) argues the rational reader's attention must be spread across all possible analyses for the sentence being observed.

while surprisal focuses on predictability effects in sentence processing, Gibson's (1998;2000) Dependency Locality Theory (DLT) focuses on the memory cost of recalling referents and integrating new ones into a mental representation.


Such features are not suited to the problem of determining the difficulty of sentences in isolation, but they have also been shown to better predict readability for second-language learners compared to 'traditional' readability measures like those described above (Crossley et al., 2011).

the parser must now keep track of two states: (1) the fact that we are still looking for a VP to complete the overall sentence; and (2) the fact that we now have a relative clause to parse before we can complete the current NP.


(2014) trained on English text produced very noisy features, as is expected for outof-domain tagging.

when a user searches for a denim brand, "DSquared2", the search engine first has to understand that the user is searching for items in the "Jeans" category.


In order to evaluate the acceptability criterion, we asked the annotators to mark each premise as acceptable if it represents undisputed common knowledge or a fact.

the results also show that the SVM model with manually created features achieves a considerably lower recall compared to precision.


We consider organizations, persons and locations separately.

in these sessions, all three annotators collaboratively analyzed arguments for resolving disagreements and obtaining a common understanding of the annotation guideline.


The argument is a generalization from one sample to the general case.

the support vector machine with manually created features significantly outperforms both the majority baseline and SVM-bow.


In addition, the feature analysis revealed that named entities and syntactic features are good indicators for separating sufficiently supported arguments from insufficiently supported arguments.

our first research question is whether human annotators can reliably apply the sufficiency criterion to real arguments and if it is possible to create annotated data of high quality.


• Sufficiency: An argument complies with the sufficiency criterion if its premises provide enough evidence for accepting or rejecting the claim.

these factors can at most masquerade an illogical argument but not improve its logical quality.


The three annotators agreed on 91.07% of all 433 arguments (observed agreement).

the CNN model is more balanced with respect to precision and recall and considerably outperforms the recall of the SVM model.


For this example, we use the 'sci' categories from the 20 Newsgroup corpus and select the 200 most frequent phrases in this subset.

the Reuters dataset presents a challenging case for descriptive clustering methods given that the distribution of gold standard categories is highly skewed, i.e., the majority categories (e.g., 'earn' and 'acq') correspond to more than one clusters while the remaining clusters cover multiple smaller categories.


We run our pipelined models for all morphological representation schemes: surface word (no token splits), lemma (no clitics), lemma with ATB clitics (contain all token splits except Al+), and lemma with D3 clitics (contains all token splits).

setup To build our sentiment and target models, we use CRF++ (Kudo, 2005) to build linear-chain sequences.


The two models are trained independently.

for each token, we add a part of speech feature.


On Twitter, the task is even more difficult, because the texts are small (only 140 characters) and are characterized by a informal style language utilized by users, many grammatical errors and spelling mistakes, slang and vulgar vocabulary, and plenty of abbreviations.

although the test set has not been released with their gold annotations, the evaluation platform of the TaSS competition is still open 1 for registered users.


NMT systems have a strong generalisation power because they encode translation units as numeric vectors that represent concepts, whereas in PBMT translation units are encoded as strings.

the experiments are run on the best PBMt and NMt constrained systems submitted to the news translation task of WMt16.


Rather, we consider clustering to be an earlier step in the overall processing pipeline (Yangarber, 2006).

w. The vector TF-IDF(d) contains TF-IDF values for its words and zeros in all other positions.


In such cases, the article's NEs are the only way to obtain a meaningful document similarity.

prominence also takes into account the total length of the document, to capture diversity of news sources in the collection.


Other representations using the relative importance of named entities in a given news article should also be considered, such as a continuous-vector representation for documents where named entities play a role.

the most important NEs are mentioned early in the text and then repeated, whereas less important NEs are mentioned in the later paragraphs and are less frequent.


First, by construction, the original scores p(d) of all web pages sum up to 1.

an approach to structurally and hence objectively assess argument relevance at web scale.


We build an argument graph for this model that we analyze with a recursive weighting scheme, adapting key ideas of PageRank.

they hardly account for the problem that argument quality (and relevance in particular) is often perceived subjectively.


Notice that the prediction problem is also present during training, in line 6 of the algorithm.

when comparing the disjoint model (SP disjoint ) with our joint model (SP) it can be noticed that joint prediction gives only a very small improvement (P=0.0768 for TLINKS, and P=0.0451 for DCTR).


The first baseline is the best performing system of the Clinical TempEval 2016 on the DCTR task (Khalifa et al., 2016).

mLN can sometimes be sub-optimal for text mining tasks w.r.t.


Such information is crucial for integrative reasoning as it includes the newest findings in specialized domains.

the availability of measurement for 20,000 human genes makes it imperative to integrate all knowledge about them, which grows rapidly and is scattered in millions of articles in PubMed 2 .


(2013), we compute the area A under the PR curves which supports this trend (EntEmb: A = 0.34, piecewise CNN: A = 0.48, contextwise CNN: A = 0.50).

average is the usual way of doing that: Multi-instance multi-label.


What happens in this seeming tautology is that the loanword changes meaning; i.e., the sense of the loanword deviates from the sense of its original word.

if the Dice coefficient of a word pair is low, the words in the pair are unlikely to be translation equivalents of each other.


As expected, almost all the differences are positive, which suggests that the difference of the word embeddings captures the meaning change.


We analyze semantic changes in loanwords from English that are used in Japanese (Japanese loanwords).

japanese loanwords from English are also important in language education (Barrs, 2013).


2 Note that although gazō is also from ancient Chinese, we focus on loanwords from English, which are usually written in katakana letters in Japanese.

the cosine similarity should be correlated to the Dice coefficient if the cosine similarity is a good indicator of meaning change.


First, a crude similarity measure between wordlists, based on Levenshtein distance, was defined and the 1% of all ASJP doculect 4 pairs with highest similarity were kept as probably related.

we chose a two-step procedure: (1) A similarity score for each pair of synonymous words from the same dataset is inferred using supervised learning, and (2) these inferred similarities are used as input for unsupervised clustering.


This explains why the combination works better than either feature on its own, but due to possible biases inherent in the makeup of the corpus, such as which female or translated authors are published and selected for the corpus, no conclusions on the influence of gender or translation should be drawn from these datapoints.

this is mitigated by the fact that fragments are extracted from pairs of folds, while selection is constrained to fragments that are attested and significantly correlated across the whole training set.


To establish whether users do modulate their usage of Scottish variants when discussing the referendum, we will need a more careful paired design.

we also find that in general people are less likely to use distinctively Scottish words in tweets with referendum-related hashtags than in their general Twitter activity.


W911NF-15-1-0543 with DARPA and the Army Research Office (ARO).

we used neural attention to obtain the "head rules" in the GA-RNNG; the whole model is trained end-to-end to maximize the log probability of the correct action given the history.


The upper tree in Figure  1 is annotated in UD.

we perform this both for the original UD and converted data, and calculate the difference for each label See Figure 3 above, where many nmods appear on the upper left side, meaning that the reduction of entropy contributes to the larger improvements cross-linguistically.


It also impacts the quantity of training data required, and the training stability.

considering these properties, we investigate the impact of the noise distribution on the training of language models.


Unfortunately, both output normalization and gradient computation require computation of the score for every word in V, which is the bottleneck during training, since it implies product of very large matrices (|V| being usually anywhere from tens to hundreds of thousand words).

the use of the uniform distribution yields more consistent results, despite the fact that it is slow to learn.


Deep learning has achieved state-of-the-art results across a range of computer vision (Krizhevsky et al., 2012), speech recognition (Graves et al., 2013), and natural language processing tasks (Bahdanau et al., 2015;Kalchbrenner et al., 2014;Bitvai and Cohn, 2015).

deep neural networks have achieved remarkable results across many language processing tasks, however they have been shown to be susceptible to overfitting and highly sensitive to noise, including adversarial attacks.


The massive amount of text provided by users of social media like Facebook and Twitter give researchers the opportunity to investigate topics that were not previously tangible.

then, we perform randomized principal components analysis (RPCA) , an approximate PCA based on stochastic re-sampling (Rokhlin et al., 2009)  We learn four different models: (1) a control model using the socioeconomic & demographic variables, (2) a language model using only tweetderived features, (3) a combined model using both socioeconomics & demographics and language in a single model, and (4) a language over residualized control model fitting language to the residual error of the control model.


By analysing the output produced after each step, we found that this is caused by the inherently high Precision of our approach: by producing a smaller number of spurious candidates, our simplifier reduces the occurrences of ungrammatical and/or incoherent substitutions, but also disregards many candidates that are simpler than the target complex word.

it can be noticed that NNLS makes many errors of type 5.


The objective is to optimise ROUGE-1 by maximising the number of word tokens paired up between system and reference summaries.

2 We use both the original 665 bytes summary budget as well as the 100 word summary budget used by (Hong et al., 2014).


Because the ROUGE optimisation problem is NP-hard, one may suspect that exchanging a greedy strategy out for an exact global approach would lead to substantial improvements in system performance.

by gold summaries, we refer to optimal summaries consisting of sentences from the input document.


As the Stanford Reader was developed for the anonymized CNN/Daily Mail tasks, only a few entries in the output embedding function needed to be well-trained in their experiments.

2 Our new dataset contains 1,827,123 instances in total.


Both architectures use a nonlinearity to modulate the contents of a product; in an RAE this is an outer (bilinear) product while in an LSTM it is a Hadamard (element-wise) product.

the gate vector is the vector centroid of the resulting word list.


2 No existing dataset treats entities and concepts on a par, which would enable a contrastive analysis of instances and concepts.


Typically a word's context is modeled as a fixed-length window of words surrounding it.

we use their software (word2vecf) to train word embeddings using geospatial contexts.


The neural tagger outperforms baselines 1 and 2 everywhere, showing that the syntactic structure of English does contain enough signal to recover the complex semantic distinctions that are overt in Czech morphology.

we use these alignments to project morphological tags from the Czech words to their English counterparts through the following process.


The rating study suggests that subjects are in fact aware of the subtle and gradient contrasts in terms of information density and indicates that their preferences mirror the corpus data.

hence, highly predictable words are less informative while highly unpredictable words are more informative.


By "immediately postverbal" we understand such nouns that are at most separated by an article and/or one adjective from the preceding verb.

bigram surprisal was chosen in order to obtain a sensible measure given the small size of the corpus.


Thus, if the occurrence of a complement clause is highly unpredictable, the insertion of a complementizer might lead to a more uniform surprisal profile by significantly reducing the high surprisal of the word(s) to follow.

we therefore shifted towards investigating our hypothesis with an acceptability rating study, which compared the acceptability of ARTICLEOMISSION as a function of SURPRISAL of a postverbal noun in constructed newspaper headlines a 2 × 2 design.


Sandig (1971), Stowell (1996), or Reich in press as well as the references cited therein.

we tested our hypothesis on the FraC corpus (Horch 2016), which is text type-balanced and has been annotated by hand for different types of ellipses.


Other studies have also focused on the phenomenon of intoxication (Schuller et al., 2014).

table 4 shows a sample of the most informative features for the four categories.


Optimization of the model is performed using stochastic gradient descent.

while U and V can both serve as word embeddings, in the literature, only the former serves this role.


We next study the effect of tying the embeddings on the perplexity obtained by the NNLM models.

we propose the following regularization scheme.


the induced meta-learning for analyzing what such characteristics are predictive of gains.

we also see that the features derived from the single task inductions are the most important.


For testing, we use the same Birkbeck spelling error corpus as used by de Amorim and Zampieri (2013).

train skip-gram word2vec model with sequences generated in the previous step with hidden layer size as n and window size as m. training yields neural embeddings for symbols present in training sequences.


Minimizing equation 6corresponds to maximum-likelihood estimation of the graphical model in Figure 1b, in which each fiber of X is viewed as being generated from a Gaussian all at once.

to the matrix case, there are several distinct definitions of tensor factorization (Kolda and Bader, 2009).


The first and last layers are acoustic feature input and predicted binary class output respectively.

this produced somewhat of an unpredictable result.


This technique is called delexicalization by Henderson et al.

the other benefit of this architecture is that it can weight its output u 1 according to how many ontology values have been detected during turn t. not all ontology values can be replaced by tags because of speech-recognition errors or simply because the ontology representation is not the same as the representation in natural language (e.g.


The re-ranker employs several features, which are enumerated in Table 4.

features 4-6 refer to the affix-match constraint defined in Section 2.1, in order to promote analyses that involve correct tags.


As regards the generally low recall on the BabelNet dataset, we found that it was mainly due to the nature of the dataset, including many isolated synsets which are hardly used in practice.

for each domain, there is a set of Wikipedia pages associated (127 on average).


While training MT systems typically requires large collections in the order of millions of words, the automatic evaluation of MT requires less data; but evaluation data is expected to have more than one human reference since there are many ways to translate from one language to another (Papineni et al., 2002).

the highest BLEU score in the X→En direction is for Maltese, and the worst BLEU score is for Hungarian again.


Our results are consistent with their claims, not only for the direct models which are similar to the models they used but also for those pivoting through English to Arabic.

the number of language pairs that are fortunate to have large parallel data is limited.


Such resources are necessary for the development of MT systems, and for the evaluation of MT systems in general.

we find the correlation between the word-per-sentence 3 in X to correlate with En→X and Ar→En→X BLEU by r = 0.82 and r = 0.91, respectively.


The latter are based on the words mentioned in the input text.

after all, having such alignments would make it difficult to judge good and bad translations, which is one of the goals of the PMB.


In this way we aim to assign 'atomic' meanings to tokens and avoid redundant lexical semantics.

we improve these models constantly by adding manual corrections to the initial automatic annotation, and retraining them.


Finally, we showed that even if the dictionaries are "translated" from the English Wiktionary through a small list of bilingual word pairs we can still predict what POS taggers are best for 11/20 languages.

there, the Wiktionaries are intrinsically evaluated with respect to the training set dictionaries.


We find that this classifier works well in the Wikipedia, but, as could be expected, performance decreases in a corpus of judgments of the European Court of Human Rights.

the O class made more than 90% of the instances, so we randomly subsampled non-named entity words to make it at most 50% of the corpus, so that classifiers would not be too biased.


All children nodes of a connected node are connected by their most immediate parent.

the fact that smaller classes are recognized better than bigger classes indicates that bigger classes are ill-delimited.


They contribute to the overall message or purpose of a text and make explicit the functional role of a discourse segment with respect to its content, i.e.

in the second phase, inter-annotator agreement was calculated on a subset of the CTD v1 (a total of 5,328 tokens and 526 clauses, with 2,500 tokens and about 250 clauses per genre).


Results are presented in the  Table 3: Results of feature ablation experiment.

the values for the dropout rate and mini-batch size were chosen using a grid search on the CCAt10 devset.


Table 3 demonstrates that removing punctuation and space n-grams leads to performance drops on both of the datasets.


It's worth mentioning that dependency annotations for our problem exhibit a significant number of non-projective arcs (26%), meaning that entities involved in the part-of relation are non-adjacent (i.e., interleaved by other entities), as intuitively expected.

transition-based dependency parsing (tB) Given that our system needs to be able to handle non-projective dependency arcs, we employ a greedy transition-based parsing system (Nivre, 2009;Bohnet and Nivre, 2012) as the basis of our parser.


For example, real estate descriptions in natural language may not be directly suited for specific search filters that potential buyers want to apply.

1 Three human annotators manually annotated 2,318 ads (1 annotation per ad, ∼773 ads per annotator) by creating the property tree of the advertisements.


The approaches proposed for this issue vary from learning a single model from pooled training data, to more complicated (log-)linear interpolations of multiple models using mixture models (Foster and Kuhn, 2007) and linear mixture models (Carpuat et al., 2014).

in Section 2 we review the state-of-the-art approaches of multi-domain machine translation for both PBMT and NMT.


The models are trained using Adagrad (Duchi et al., 2011), reshuffling the training corpora for each epoch.

it is interesting to see what is the reason behind the very low performance of the generic NMT system compared to the generic PBMT.


We apply the following operations: • Remove: remove a random date and its summary.

different datings of the same event are not penalized.


Our method EncDec+WFE successfully achieved the current best scores on most evaluations.

experiments on ABS benchmark data showed the effectiveness of our method, encDec+WFe, for both improving automatic evaluation performance and reducing the actual redundancy.


Then, in Line 13, the function selectTopK selects the top-K candidates from union of a set of generated triplets at current step {h z } K−C z=1 and a set of triplets of complete sentences in Q c .

decoding with our method never generates words that exceed the estimationr, and thus we expect to reduce the redundant repeating generation.


More in general, 7 out of 10 modified songs were rated as being as grammatical as the originals.

non-content words are not modified and, when multiple substitutions are possible, the system chooses the one that better fits the context, according to a language model (Brants and Franz, 2006).


Given the previous example, we obtain words such as "mum", "mummy", "mama" (synonyms of "mom"), "hound" (from "dog"), the nouns "chomp" and "bite" and the verbs "to munch" and "to chew" (all from the verb "to bite").

it looks for capitals (for countries), countries (for cities or regions) and demonyms (for all the geographical locations), while for people it extracts names, surnames, occupations and fields of work.


For the embedding representations of these two types of features, we use the embeddings of de Kok (2015), which were trained on corpora of 800 millions tokens, using WANG2VEC (Ling et al., 2015), a variation of WORD2VEC that is tailored to syntactic tasks.

(2007) that counts the number of words between the preposition and the candidate, with the logarithm of this distance and the relative distance.


We train POS tagging models using a similar neural network architecture as dis-cussed above.

we use Giza++ (Och and Ney, 2003) to character align the transliteration pairs for training.


Parsing Models Our parsing model is based on transition-based dependency parsing paradigm (Nivre, 2008).

against an informed monolingual baseline, our parsing strategies are at-least 10 LaS points better.


The training data consists of English-German triplets containing source English text (src) from the IT domain, corresponding German translations (mt) from a firststage MT system and the corresponding human post-edited version (pe).

each mt-pe word alignment also gets a unique identification number (a id ) and a vector representation is generated for each such a id .


Regardless of the predictability of automatic metric scores when evaluated with MAE, they unfortunately do not provide a suitable gold standard, simply because they are known to provide an insufficient substitute for human assessment, often unfairly penalizing translations that happen to be superficially dissimilar to reference translations (Callison-Burch et al., 2006).

the final values used to produce official results were W 1 = 1 and W 2 = 13.


We argue that MBR-based methods in their present form are not well-suited for NMT because of the following reasons: • Previous approaches work well with rich lattices and diverse hypotheses.

(2008) has the form where Y h is the hypothesis space of possible translations, Y e is the evidence space for computing the Bayes-risk, and N is the set of all n-grams in Y e (typically, n = 1 .


Factored machine translation with separate translation and generation models represents a different approach, operating with a singlestep search.

a phrase-based model with linguistically motivated factors       In our baseline systems, we already draw on lemmas and morphosyntactic tags as factors on the target side, in addition to word surface forms.


The models with character decoder make fewer transliteration errors than the BPE-to-BPE model.

in the first one, our method automatically recognizes an error that also appears in the 1-best translation.


0.149 contrastive Seitdem haben wir nur in der Schwedischen Liga gespielt, die nicht das gleiche Niveau haben.

they perform more poorly on separable verb particles and agreement, especially as distance increases, as seen in Figure 1.


In the following, we attach a dynamic memory vector to the original static memory h i , to keep track of how many times this word has been attended to and whether the neighboring words are selected at previous time steps, the information, together with h i , is used to predict the next word to select.

we use bidirectional LSTMs to encode the source sentences.


The exponential weighting assigns more importance to the top words in the list which are semantically more representative of w r .

in (Pilehvar and Collier, 2016b), we proposed a model that exploited Wikipedia articles in order to adapt a set of pre-trained embeddings to a specific domain.


(w animal , w cat , negative), or pairs with related words, e.g.

we balance the test sets using an under-sampling scheme.


Plagiarism is a statement that someone copied text deliberately without attribution, while these methods only detect textual similarities.

while the use of bilingual sentence vector (CL-wES) is simple and elegant, its performance is lower than three state-of-the-art methods.


Unsurprisingly, the lemma component has a positive effect on semantics, while the tag component improves the morphological performance.

the poor results for the base-only models were mainly attributed to undesirable capturing of derivational similarity, e.g.


in this work, we explore ways to scale these baselines to very large corpus with a large output space, in the context of text classification.


Inspired by the approach of Dredze et al.

our standard treatment defines a document as one full article, but we additionally train models on a segmented version of the corpus (NYT-S) where each article is broken into 100-word segments.


A distribution with a single large bucket and few small buckets would signify better coherence.

tBuckets uses singular value decomposition (SVD) to discover important themes in topic words and ILP based optimization to find optimal word-bucket assignments.


This may lead to splitting of the central theme across multiple buckets and hence words that should align with the central theme may get aligned to other (related) themes.

constraint C 4 ensures that there can be only two types of words in the principal bucket: i) words for which the prin-cipal eigenvector is the most similar and ii) words for which the principal eigenvector is the second most similar.


very negative, negative, neutral, positive, very positive) from Socher et al (2013).

the noisy dataset is a 5-classes review dataset from Yelp (tang et al., 2015).


For instance, in softmax, each input is simply weighted by its contribution to the sum.

when compared to LSTM and CNN/LSTM models, GRA with alignment produces a consistently larger union of correct cases (typically by 5-10%) than GRA without alignment.


Recently, there has been a lot of activity in learning distributed representations of words in vector spaces.

section 2 provides an overview of the related work.


Latent Semantic Analysis (Deerwester et al., 1990) and Latent Dirichlet Allocation (Blei et al., 2003) are the main employed methods for this task.

to perform text categorization, we employed a linear SVM classifier.


We proposed an approach that models each document as a Gaussian distribution based on the embeddings of its words.

for our experiments, we used a publicly available model 1 M consisting of 300-dimensional vectors trained on a Google News dataset of about 100 billion words.


This paper proposes a novel distributed representation of a document, which we call "document vector" (DV).

the Brown Corpus has a relatively strong overlapping between topics and genres.


Each meeting comes with gold standard in the form of human-written abstractive and extractive summaries.

previous work did focus on offline meeting summarization.


This paper extends recent work examining the utility of distributed state representations for taskoriented dialogue agents, without providing rules or manually tuning features.

the present literature lacks results for now standard sequence-to-sequence architectures, and we aim to fill this gap by building increasingly complex models of text generation, starting with a vanilla sequence-to-sequence recurrent architecture.


In addition, players can negotiate trades with each other in order to obtain the resources they desire.

it has been shown that RL dialogue agents can be trained to strategically select offers in trading dialogues (Keizer et al., 2015;Cuayahuitl et al., 2015c), but also to bluff and lie (Efstathiou and Lemon, 2015;Efstathiou and Lemon, 2014).


To the best of our knowledge, the best performing supervised system on Switchboard-DAMSL corpus is presented in (Kalchbrenner and Blunsom, 2013) and achieves accuracy of 73.9%.

some low level characteristics have already been clearly identified, e.g.


(she moved there five years ago) [continuative] The resultative meaning in (1b) is thought to constitute the core of the PERFECT.

we believe including these examples in the current pilot study, with a limit dataset and only five languages in total, would have a negative effect on our analyses.


This setting can be actually explained by that the output values of the two functions are between 0 to 1, which is used to decrease the learning rate.

we let the CBOw threads to control convergence; training stops when CBOw threads finish processing the data.


(2013a), where we learn a translation matrix M ∈ R dt×ds (where d s is the size of word embeddings of the source and d t of the target language) that projects source language embeddings into the embedding  , M is obtained by minimizing the following objective: The obtained matrix M can then be used to map the embedding of any word from the source language to the embedding space of the target language.

we can translate source language triples (e s 1 , r, e s 2 ) into the corresponding target language triples (e t 1 , r, e t 2 ) for each target language.


Since compositionality detection also provides a way of evaluating compositional methods without confounding judgements of phrase similarity with judgements of compositionality, it appears that the APT approach to composition is reasonably promising.

any judgements of the similarity of two composed phrases may be confounded by the degree to which those phrases are compositional.


In relation to the construction of the elementary APTs, the most obvious parameter is the nature of the weight associated with each feature.

a small but statistically significant performance gain is generally made using the hybrid model.


(2004) introduced a method for learning patterns automatically based on a set of seed hyponym-hypernym pairs.

drawing negative examples uniformly from the vocabulary turned out to hamper performance.


We propose to use the attention-based encoderdecoder for multi-target stance classification.

we followed Luong et al., 2015) to train our models using the minibatch stochastic gradient descent (SGD) algorithm with adaptive learning rate (Adadelta (Zeiler, 2012)).


Previous work focus on learning a target specific representation for a given input sentence which is used for classification.

the first is a contextualized attention model (BILStM-Att-C), where the sentence is divided into two segments with respect to the target, namely left context and right context tang et al., 2016;.


The READER results in significantly higher correlation, 5 but also higher error than  Prior work found that a large portion of language may actually be neutral in terms of emotion (Ovesdotter Alm et al., 2005).

we capture this notion as the absolute difference of a sentence's aggregated rating from the neutral rating (3, in our case), averaged over all VAD dimensions.


In Figure 3b, both GBMT and GBMT ctx choose to translate the subgraph WoMen Ye ZhiLi.

c a i = * means that a basic rule is applied without considering context to make sure that at least one translation is produced for any inputs during decoding.


Those approaches differ in the type of data they can process (monolingual data, word-aligned parallel data, parallel sentence pairs, comparable documents).

this test set is more representative of a real-life setting.


The objective of bilingual lexicon induction is to find translation pairs between two languages.

we exclude pairs that contain a word which is already in the lexicon.


Instead, we add a classifier feature and compare two variants: (i) setting the feature to a binary support verb status (yes/no) for a fixed set of tuples (using a log-likelihood threshold of 1000, as in Cap et al.

sein ('to be') vs. haben ('to have') as auxiliary for past tense.


We use Adam (Kingma and Ba, 2015) for optimization.

2 Here the behavior is similar, but unlike the Swedish model which got somewhat better when closely related languages were added, the increase in cross-entropy is monotone.


As shown by the small oracle experiments with manually inserted annotations, the potential for improvement through co-reference resolution is significant.

she believed that her family's sacrifices were justified by the liberation and rise of China.


The Micro-precision scores (not shown here) are very similar to the micro-F1 scores with occasional differences in the third and fourth decimal places.

from our experiments, the ratio turned out to be 0.37, which means that GBT and ACNN make different mistakes more than 60% of the time.


We employ Adaptive Moment Estimation (Kingma and Ba, 2015) with a learning rate of 1e − 4 to train our network.

where the embedding e represents the input and the class score S c represents the output of our CNN model.


Previous work has shown that it is difficult for any AA system to maintain the same performance with shorter texts (Koppel and Winter, 2014).

despite the competitive performance of neural representation techniques in several NLP tasks, there is a lack of understanding about exactly what these models are learning, or how the parameters relate to the input data.


In order to analyze what makes neural representation learning suitable for AA, we look at the most salient sections of a single input tweet.

we were able to find research in AA using traditional as well as related approaches.


For this purpose, we use Support Vector Machines (SVMs) with a linear kernel in four different experiments.

when the full set of features was used, the accuracy improved to 91.41%.


no n-grams: While other systems heavily relies on n-grams, our model maps each token to a token embedding, and feeds it as an input to an Rnn.


Over 50 million scholarly articles have been published (Jinha, 2010), and the number of articles published every year keeps increasing (Druss and Marcus, 2005;Larsen and Von Ins, 2010).

traditional sentence classification approaches have been shown to greatly benefit from jointly classifying subsequent sentences, such as with conditional random fields.


It computes the mean rating of the top-ranked label generated by the system, and provides an assessment of the absolute utility of the labels.

the improvement compared to disjoint-NN is substantial, and much of the improvement is in the textual labels.


We can treat word segmentation as discrete features in neural network model.

we make contributions as follows: • We propose a method to directly train on F-Score rather than label accuracy.


Collobert and Weston (2008) trained a convolutional NN with MTL which, given an input sentence, could perform many sequence labeling tasks.

we tested different dropout rates (0.2, 0.4) for the input and (0.3, 0.5, 0.7) the hidden layers obtaining better results with highest values, i.e., 0.4 and 0.7.


Table 3 shows that the overall trend is similar to what is observed in Table 2.

more importantly, using representations avoids leaking sensitive information delivered by lexical terms.


h is constructed by concatenating word and user embeddings in the order shown in Figure 1, defined as where v s , v j and v r are embeddings of the sender, content words and recipients, respectively.

our baseline approaches take into account two most effective setups from prior work where the combination of email content and metadata is featurized.


summarization (Carenini et al., 2008), and spam filtering (Gee, 2003) all depend on properly representing the content of the message body, which then can be consumed in the target tasks.

for each target user u i , we first identify all others {u j | j =i } that he/she has had communications with, and then bucket the cosine similarity between their embeddings into intervals.


In Section 3, we describe and analyze our experiments.

given a corpus containing these two sentences, fine-grained entity typing should predict the types {"politician", "lawyer"} for "Obama".


In the case that more than one finite verb is found in a sentence, the first finite verb which has agents and/or actions in the sentences is used as a value for that sentence.


In most of cases, the improvements is significant.

generally, dependency structure analysis consists of two steps.


In this experiment, we simply give an equal weight to each classifier.

these approaches have overcome the systems based on the rule-based approaches.


We decided to take into consideration all such modification information by using functional words or inflection forms of modifiers.

when a sentence is long and there are more than one possible dependents, static features, by themselves cannot determine the correct dependency.


To measure the uncertainty of a sentence more accurately, the evaluation function must base its estimation on the outcome of testing the sentence on the hypothesis-grammar.

appropriate supervised training data may be difficult to obtain.


For instance, in (2), we find the exact same sequence of parts of speech, but (2a) is a particle use of on, while (2b) is a prepositional use.

the percentage of the same type of error for known words is 16.2%.


If that by the context model is bigger (i.e.

a linear contribution of dot products to the probability results in poorer estimates, since the differences of dot products of related words (tend to co-occur) and unrelated words are not so large.


For Spanish, we downloaded a bilingual English-Spanish lexicon from the Internet (http://www.activa.arrakis.es) containing around 22,000 English words (16,000 English stems) and processed it similarly.

for the query set Trec4S, the percentage is 12%.


In our method, those English translations most likely co-occur with each other can be obtained via looking up the mutual information value matrix of the English corpus with a cutoff M1 = 1.50.

of our query translation method, an English query is constructed in which each query term has a weight.


The second tree is a default tree that just classifies any sentence as not an answer.

the first tree states that if the Diff-from-Max-Word-Match is zero (i.e., the sentence has the highest number of word match to the question), then the sentence is an answer.


Prior to 1999, the other notable research work on question answering that is designed to work on unrestricted text (from encyclopedia) is (Kupiec, 1993).

there was also an owl and a young pig, called a piglet.


To evaluate our learning approach, we trained AQUAREA$ on the same development set of stories and tested it on the same test set of stories as those used in all past work on the reading comprehension task (Hirschman et al., 1999;Charniak et al., 2000;Riloffand Thelen, 2000;Wang et al., 2000).

the possible values for these features are true or false.


Within the 60 test stories, there are 59 who questions, 61 what questions, 60 when questions, 60 where questions, and 60 why questions, for a total of 300 test questions.

we adopt a machine learning approach in this paper.


Our goal is to design a feature vector representation such that it provides useful information to a learning algorithm to automatically build five classifiers, one for each question type.

their features are meant for the task of selecting more specific answer spans, and are different from the features we use in this paper.


In this section, we evaluate the performance of each six heuristics as well as the combination method.

the first sense of 'gwan-mog" has five candidate synsets.


This observation allows both to identify the noisy examples and use LazyBoosting as a way to improve data quality.

this modification significantly increases the efficiency of the learning process (empirically, up to 7 times faster) with no loss in accuracy.


The predicates used, which are the binarization of the attributes described in section 3.2, are of the form "f = v", where f is a feature and v is a value (e.g: "-r v" p e mus_word = hospital").

the relation between X+%Y-Y and %Y-Y reveals some interesting facts.


The variety of sizes and distribution among the types of NEs shows that using search engines with different capabilities yields different figures for the collections of pages.

we will not discuss these in more detail here.


In the case of the German Verbmobil corpus, the data structures are not trees, since structures as in Fig.

the columns pl, p2, dl, d2, 11 and 12 stand for binary relations and have values 1 or 0 depending on whether the relation holds or not.


The tool uses a query language that allows to search for tokens, syntactic categories, grammatical functions and binary relations of (immediate) dominance and linear precedence between nodes.

complex syntac-tic structures involving category and edge labels and binary relations can be searched.


If there is evidence to the contrary, H0 is rejected and the alternative hypothesis (H1) is accepted.

since the BNC is a heterogeneous corpus we felt it was reasonable to test the data on a different corpus, which is also heterogeneous.


The results are shown in Tables 4 and 5.

future work that takes into account the conclusions drawn in this paper will perhaps be able to automatically extend the number of examples available and tackle the acquisition problem.


The results for WSJ indicate that drawing training and testing data from the same or different documents in itself does not affect so much the results.

in order to analyze and compare the behavior of several kinds of collocations (cf.


Potential SCF entries are filtered before the final SCF lexicon is produced.

this suggests that we can obtain more accurate back-off estimates for verbal acquisition by basing them on a semantic verb type.


When trained as a single classifier (e.g., (Roth and Zelenko, 1998) uses each -tagged example as a positive example for and a negative example for all other tags.


For the automatic classification task, we used only the 18 relations (indicated in bold in Table 1) for which an adequate number of examples were found in the current collection.

we find that a very simple approach using a machine learning algorithm and a domain-specific lexical hierarchy successfully generalizes from training instances, performing better on previously unseen words than a baseline consisting of training on the words themselves.


In the training set, these nouns appear only in NCs that have been labeled as belonging to relation 22.

our approach handles "mixed-class" relations naturally.


Using the same approach, we evaluation tractable.

mWUs are typically non-compositional at some linguistic level.


Text markers and cue words are discussed in section 2.4.

they include characters such as '(…)', '[…]', and '='.


A sequence of words in the contexts is considered as a candidate definition if it satisfies the following conditions.

the definition is encoded in "www" because "Near-End" is divided in two words "Near" and "End.


Furthermore, the abbreviation matcher consists of 5 simple match routines and each routine is dedicated to a certain type of abbreviations.

some examples of definitions and their patterns are in Table 2.


our results indicate that the standard probability model does not solve grapheme-to-phoneme conversion su ciently although, we varied all free parameters of the probabilistic reestimation procedure.


This may underestimate the difculty of the Brown corpus by including sentences from the same documents in training and test sets.

because the word pair feature is the most speci c in the model, it is likely to be the most corpusspeci c. The vocabularies used in corpora vary, as do the word frequencies.


Finally, model structure and parameter estimation are not the entirety of factors which determine the behavior of a model.

we performed the following wSD experiments with Naive-Bayes models.


It would be interesting to try combining this algorithm with decision trees in some way.

if there is wind, however, the smart weatherman says, "50% chance of rain tomorrow; my entropy is 1 bit."


We describe a simple, incremental algorithm that avoids this problem, and show how to implement it efficiently.

our new algorithm can be thought of as a probabilistic version of the Ramshaw and Marcus (1994) algorithm, for speeding up TBLs.


9 To evaluate systems on the full disambiguation task, it is appropriate to compare them on their accuracy at 100% testdata coverage, which is equivalent to system recall in the official SENSEVAL scores.

second, it is possible that the problem can be decomposed into orthogonal feature spaces (e.g.


A very common technique for combination in such a case is by voting (Brill and Wu, 1998;van Halteren et al., 1998;Sang et al., 2000).

it can also be useful to consider performance on only the subset of data for which a system is confident enough to answer, measured by the secondary measure precision.


At each round, Ý classifiers are built using as training all the examples in the training set except the one to be classified, and the weights are updated at feature level rather than context level.

we first present an original classification correction method based on the variation of posterior probability estimates across data and then the adaptation of the Adaboost method (Freund and Schapire, 1997) to the task of lexical classification.


All learning parameters use the default values in WEKA unless otherwise stated.

accuracy for each word task  Recall is very close (but not always identical) to precision for the top SENSEVaL participating systems.